1887

Abstract

Antimicrobial resistance (AMR) is a significant public health threat. Low-cost whole-genome sequencing, which is often used in surveillance programmes, provides an opportunity to assess AMR gene content in these genomes using approaches. A variety of bioinformatic tools have been developed to identify these genomic elements. Most of those tools rely on reference databases of nucleotide or protein sequences and collections of models and rules for analysis. While the tools are critical for the identification of AMR genes, the databases themselves also provide significant utility for researchers, for applications ranging from sequence analysis to information about AMR phenotypes. Additionally, these databases can be evaluated by domain experts and others to ensure their accuracy. Here we describe how we curate the genes, point mutations and blast rules, and hidden Markov models used in NCBI’s AMRFinderPlus, along with the quality-control steps we take to ensure database quality. We also describe the web interfaces that display the full structure of the database and their newly developed cross-browser relationships. Then, using the Reference Gene Catalog as an example, we detail how the databases, rules and models are made publicly available, as well as how to access the software. In addition, as part of the Pathogen Detection system, we have analysed over 1 million publicly available genomes using AMRFinderPlus and its databases. We discuss how the computed analyses generated by those tools can be accessed through a web interface. Finally, we conclude with NCBI’s plans to make these databases accessible over the long-term.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000832
2022-06-08
2022-06-28
Loading full text...

Full text loading...

/deliver/fulltext/mgen/8/6/mgen000832.html?itemId=/content/journal/mgen/10.1099/mgen.0.000832&mimeType=html&fmt=ahah

References

  1. Antimicrobial Resistance Collaborators Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022; 399:629–655 [View Article] [PubMed]
    [Google Scholar]
  2. Rehman MA, Yin X, Persaud-Lachhman MG, Diarra MS. First Detection of a Fosfomycin Resistance Gene, fosA7, in Salmonella enterica Serovar Heidelberg Isolated from Broiler Chickens. Antimicrob Agents Chemother 2017; 61:e00410-17 [View Article] [PubMed]
    [Google Scholar]
  3. Mellmann A, Bletz S, Böking T, Kipp F, Becker K et al. Real-time genome sequencing of resistant bacteria provides precision infection control in an institutional setting. J Clin Microbiol 2016; 54:2874–2881 [View Article] [PubMed]
    [Google Scholar]
  4. Zhao S, Tyson GH, Chen Y, Li C, Mukherjee S et al. Whole-genome sequencing analysis accurately predicts antimicrobial resistance phenotypes in Campylobacter spp. Appl Environ Microbiol 2016; 82:459–466 [View Article] [PubMed]
    [Google Scholar]
  5. Allard MW, Bell R, Ferreira CM, Gonzalez-Escalona N, Hoffmann M et al. Genomics of foodborne pathogens for microbial food safety. Curr Opin Biotechnol 2018; 49:224–229 [View Article] [PubMed]
    [Google Scholar]
  6. Moran RA, Anantham S, Holt KE, Hall RM. Prediction of antibiotic resistance from antibiotic resistance genes detected in antibiotic-resistant commensal Escherichia coli using PCR or WGS. J Antimicrob Chemother 2017; 72:700–704 [View Article] [PubMed]
    [Google Scholar]
  7. Nayfach S, Páez-Espino D, Call L, Low SJ, Sberro H et al. Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome. Nat Microbiol 2021; 6:960–970 [View Article] [PubMed]
    [Google Scholar]
  8. Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 3:160018 [View Article] [PubMed]
    [Google Scholar]
  9. Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ et al. Validating the AMRFinder tool and resistance gene database by using antimicrobial resistance genotype-phenotype correlations in a collection of isolates. Antimicrob Agents Chemother 2019; 63:11 [View Article] [PubMed]
    [Google Scholar]
  10. Feldgarden M, Brover V, Gonzalez-Escalona N, Frye JG, Haendiges J et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep 2021; 11:12728 [View Article] [PubMed]
    [Google Scholar]
  11. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990; 215:403–410 [View Article] [PubMed]
    [Google Scholar]
  12. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J et al. BLAST+: architecture and applications. BMC Bioinformatics 2009; 10:421 [View Article] [PubMed]
    [Google Scholar]
  13. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 2020; 75:3491–3500 [View Article] [PubMed]
    [Google Scholar]
  14. Seemann T. tseemann/abricate; 2021 https://github.com/tseemann/abricate
  15. Eddy SR. Accelerated Profile HMM Searches. PLoS Comput Biol 2011; 7:10 [View Article] [PubMed]
    [Google Scholar]
  16. Bush K, Jacoby GA. Updated functional classification of beta-lactamases. Antimicrob Agents Chemother 2010; 54:969–976 [View Article] [PubMed]
    [Google Scholar]
  17. Li W, O’Neill KR, Haft DH, DiCuccio M, Chetvernin V et al. RefSeq: expanding the Prokaryotic Genome Annotation Pipeline reach with protein family model curation. Nucleic Acids Res 2021; 49:D1020–D1028 [View Article] [PubMed]
    [Google Scholar]
  18. Partridge SR, Di Pilato V, Doi Y, Feldgarden M, Haft DH et al. Proposal for assignment of allele numbers for mobile colistin resistance (mcr) genes. J Antimicrob Chemother 2018; 73:2625–2630 [View Article] [PubMed]
    [Google Scholar]
  19. Tyson GH, Li C, Hsu CH, Ayers S, Borenstein S et al. The mcr-9 gene of Salmonella and Escherichia coli is not associated with colistin resistance in the United States. Antimicrob Agents Chemother 2020; 64: [View Article]
    [Google Scholar]
  20. Carroll LM, Gaballa A, Guldimann C, Sullivan G, Henderson LO et al. Identification of novel mobilized colistin resistance gene mcr-9 in a multidrug-resistant, colistin-susceptible Salmonella enterica Serotype Typhimurium Isolate. mBio 2019; 10:e00853-19 [View Article] [PubMed]
    [Google Scholar]
  21. Kieffer N, Royer G, Decousser J-W, Bourrel A-S, Palmieri M et al. mcr-9, an inducible gene encoding an acquired phosphoethanolamine transferase in Escherichia coli, and its origin. Antimicrob Agents Chemother 2019; 63:e00965-19 [View Article] [PubMed]
    [Google Scholar]
  22. Osei Sekyere J, Reta MA. Global evolutionary epidemiology and resistome dynamics of Citrobacter species, Enterobacter hormaechei, Klebsiella variicola, and Proteeae clones. Environ Microbiol 2021; 23:7412–7431 [View Article] [PubMed]
    [Google Scholar]
  23. Coordinators NR. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2016; 44:D7–19 [View Article] [PubMed]
    [Google Scholar]
  24. Zhang A-N, Gaston JM, Dai CL, Zhao S, Poyet M et al. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nat Commun 2021; 12:4765 [View Article] [PubMed]
    [Google Scholar]
  25. Kyriakidis I, Vasileiou E, Pana ZD, Tragiannidis A. Acinetobacter baumannii antibiotic resistance mechanisms. Pathogens 2021; 10:373 [View Article] [PubMed]
    [Google Scholar]
  26. Sharkey RE, Herbert JB, McGaha DA, Nguyen V, Schoeffler AJ et al. Three critical regions of the erythromycin resistance methyltransferase, ErmE, are required for function supporting a model for the interaction of Erm family enzymes with substrate rRNA. RNA 2022; 28:210–226 [View Article] [PubMed]
    [Google Scholar]
  27. Haft DH, DiCuccio M, Badretdin A, Brover V, Chetvernin V et al. RefSeq: an update on prokaryotic genome annotation and curation. Nucleic Acids Res 2018; 46:D851–D860 [View Article] [PubMed]
    [Google Scholar]
  28. The Federal Task Force on Combating Antibiotic-Resistant Bacteria National action plan for combating antibiotic-resistant bacteria 2020-2025; 2020
  29. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res 2020; 48:D517–D525 [View Article] [PubMed]
    [Google Scholar]
  30. Mahfouz N, Ferreira I, Beisken S, von Haeseler A, Posch AE. Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review. J Antimicrob Chemother 2020; 75:3099–3108 [View Article] [PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000832
Loading
/content/journal/mgen/10.1099/mgen.0.000832
Loading

Data & Media loading...

Supplements

Supplementary material 1

EXCEL

Most cited this month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error